Triple
T4660938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clark/Lake |
E102527
|
entity |
| Predicate | hasAdjacentStation |
P231
|
FINISHED |
| Object | Grand (Blue Line) |
E406955
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Grand (Blue Line) | Statement: [Clark/Lake, hasAdjacentStation, Grand (Blue Line)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grand (Blue Line) Context triple: [Clark/Lake, hasAdjacentStation, Grand (Blue Line)]
-
A.
Grand (Red Line)
chosen
Grand is a Chicago 'L' rapid transit station on the Red Line located in the city's Near North Side.
-
B.
Blue Line
The Blue Line is one of Boston's MBTA rapid transit routes, running primarily between downtown Boston and the coastal communities of East Boston and Revere.
-
C.
Blue Line
The Blue Line is one of the main lines of the Lisbon Metro system, serving key central and northern areas of Portugal’s capital city.
-
D.
Blue Line
The Blue Line is one of the primary routes of the MetroLink light rail system serving the St. Louis metropolitan area.
-
E.
Blue Line
The Blue Line is one of the main rapid transit corridors of the Hyderabad Metro system in Hyderabad, India.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd632a17cc8190bcdab0a13b89f5c0 |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaf9564c819094b9340570a7616b |
completed | March 21, 2026, 1:57 a.m. |
Created at: March 20, 2026, 1:15 p.m.